CN107172682B - Ultra-dense network wireless resource allocation method based on dynamic clustering - Google Patents

Ultra-dense network wireless resource allocation method based on dynamic clustering Download PDF

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CN107172682B
CN107172682B CN201710556079.XA CN201710556079A CN107172682B CN 107172682 B CN107172682 B CN 107172682B CN 201710556079 A CN201710556079 A CN 201710556079A CN 107172682 B CN107172682 B CN 107172682B
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cluster
user
resource block
resource
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CN107172682A (en
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刘旭
周耀
朱晓荣
杨龙祥
朱洪波
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Jiangsu Hengxin Technology Co Ltd
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Nanjing University of Posts and Telecommunications
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
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Abstract

The invention discloses a dynamic clustering-based ultra-dense network wireless resource allocation method, which comprises the following steps: the dynamic clustering process of the base station, carry on the dynamic clustering to the base station that is distributed randomly in the network, carry on the clustering of a large number of base stations in the network through the clustering method of improved K mean value, offer the effective allocation space for resource block allocation in the cluster of users of different modes; and in the resource block allocation process, according to the clustering result in the step one, joint processing is carried out on single base station resource allocation of a central user and in-cluster CoMP resource allocation of edge users, and through the proposed proportional fairness-based resource block allocation method, in a cluster where the users are located, resource blocks with better channel states of the base stations are preferentially allocated, meanwhile, the received interference is reduced, the proportional fairness among the users in different modes is ensured, and the optimal resource block allocation result is obtained. The method of the invention can effectively improve the user rate of the system and achieve the final goal of optimizing the whole network resources.

Description

Ultra-dense network wireless resource allocation method based on dynamic clustering
Technical Field
The invention relates to a dynamic clustering-based ultra-dense network wireless resource allocation method, belonging to the field of base station clustering and resource allocation in a wireless communication system.
Background
With the exponential growth in the number of users and bandwidth requirements, mobile cellular networks face significant challenges. To increase overall network capacity and meet explosive data traffic demands, one possible approach is to increase the density of network access points, i.e., to deploy dense small stations within the macro station coverage area. With the gradual increase of the node density of the Network, an Ultra Dense Network (UDN) is formed finally. The ultra-dense network breaks through the inherent pattern of the traditional cellular network, and a large amount of data traffic is shunted from the macro cell to the micro cell, so that the optimal allocation of wireless resources in the whole coverage area can be more effectively realized. At the same time, such networks also cause strong signal interference between microcells and severely affect the performance of cell-edge users. To solve these problems, Coordinated Multi-Point transmission and Reception (CoMP) can convert an interference signal into a useful signal to mitigate Inter-cell interference (ICI). Considering the maximization of user throughput, the network adopts a non-overlapping clustering mode at the same time. However, the existing clustering methods are limited to maximize the performance of a single user, and neglect the limitation of intra-cell and inter-cell radio resource allocation caused by CoMP transmission.
The existing access scheme related to dynamic clustering cannot adapt to the environment of an ultra-dense network. For example, one resource-aware-based access policy is to select a cell by the resource utility of each user; the other distributed access strategy achieves load balancing by maximizing the full network utility; an improved access strategy is proposed, taking into account the load and special constraints of the micro base stations to assist the access selection. However, the above studies have mainly focused on single cell selection and optimization of resource allocation, and have not considered the performance of inter-cell CoMP transmission in the initial cell selection phase. On the other hand, in the field of radio resource Allocation research, a dynamic spectrum Allocation method (FFAP) is only applicable to non-overlapping clustering situations because the Allocation of subchannels is based on different clusters; in the problem of optimizing multi-cell subchannel allocation by a greedy search iteration algorithm, the algorithm complexity increases dramatically as the number of nodes and users increases. Neither of these methods can be used to measure resource allocation performance in UDN scenarios. The method performs combined optimization on the performance of the wireless network under the UDN scene by coordinating the interference among the cells and combining two processes of access node selection and resource allocation in the cells. Due to the condition constraint of resource scheduling in the scene, a step-by-step joint access allocation method must be adopted. For the access process of the base station, the constraint condition of the CoMP mode needs to be considered heavily, for example, the differential load condition of each cell in a cluster. For the resource allocation process, a wireless network virtualization concept can be used, and the physical resources of the air interface link are regarded as a two-dimensional grid of time and frequency, that is, a resource pool is formed. The radio resource is divided into resource blocks, each of which has 12 subcarriers in the frequency domain and 7 OFDMA symbols in the time domain. Each resource block represents different frequency points and time slots, and the attributes such as interference, time delay, power and the like are different. The user accesses to the wireless network, and uses a certain resource block to transmit data, and the more resource blocks are obtained, the more bandwidth and time slot can be obtained, the higher the transmission rate and time length are, and the better the quality of service is. In the resource allocation process, the problem of inter-cell interference is heavily considered. The invention provides an iterative algorithm suitable for a dense network, and obtains a larger signal-to-interference-and-noise ratio and an overall system throughput by relieving inter-cell interference.
Ultra-dense network resource allocation has become a key and difficult point of wireless network research in recent years. Research shows that the problem of interference in a network needs to be considered in a key way for resource allocation in an ultra-dense wireless network, and most of the existing methods adopt the modes of resource partitioning or transmission power control of the network, interference suppression of a mobile station and the like. However, these methods do not consider allocation from the perspective of resource blocks, and do not consider the characteristics of wireless channels and the performance of edge users, so it is necessary to implement efficient resource allocation in combination with the constraint conditions of all resource allocation, thereby further improving the performance of the network.
Disclosure of Invention
In order to overcome the defects of insufficient dynamic property and weak expansibility in the conventional resource allocation method, the technical problem to be solved by the invention is to provide a dynamic clustering-based ultra-dense network wireless resource allocation method. The method divides the whole process of wireless resource allocation into two sub-processes of dynamic clustering and resource block allocation: in the clustering process, the self-adaptive clustering of the dense small base stations is solved by using an improved K-means clustering method, and the clustering result of the most suitable random distribution scene is obtained; in the resource block allocation process, for the clustering result obtained in the previous sub-process, a proportional fair-based resource block allocation method is used for resource block allocation, the resource blocks of all base stations in the virtual resource pool are allocated to all users, and edge users are processed according to the allocation requirement of multipoint cooperative transmission. The method of the invention relieves the same frequency interference by dynamic and efficient cluster division and adjacent cluster resource block division, effectively relieves the problem of the same frequency interference among cells in the traditional static method in the ultra-dense network environment, improves the flexibility of resource allocation, improves the utilization rate of an underlying network, reduces the load of physical nodes or physical links, and finally achieves the purpose of improving the network performance.
The invention adopts the following technical scheme for solving the technical problems:
the invention relates to a dynamic clustering-based ultra-dense network wireless resource allocation method, which comprises the following specific steps:
step one, dynamic clustering of a base station: determining cluster center points and the number of clusters according to the distribution density of the base stations, and clustering the base stations which are randomly distributed in the network by adopting an improved K-means clustering method;
step two, resource block allocation: and B, according to the clustering result in the step one, adopting a proportional fair-based resource block allocation method to allocate resource blocks, allocating the resource blocks of all base stations in the virtual resource pool to all users, and processing edge users according to the allocation requirement of multipoint cooperative transmission, thereby completing resource allocation.
As a further technical scheme of the invention, the super-dense network is densely distributed LTE small base stations, wherein a base station space distribution model is an independent uniform poisson point process in a two-dimensional plane, and all base stations adopt an OFDMA access mode.
As a further technical scheme of the invention, the first step is specifically as follows:
1.1: calculating the density index of each base station in the network:
Figure GDA0002449805380000031
wherein D isfIs a density index of the f-th base station, rfIs the neighborhood radius of the f-th base station, scfIs the coordinate of the f-th base station, scbIs the coordinate of the b-th base station, and F is the total number of base stations in the network;
1.2: the base station with the maximum density index value is recorded as
Figure GDA00024498053800000314
Its density is marked as
Figure GDA00024498053800000315
Then
Figure GDA00024498053800000316
Defining the jth cluster center point, and updating the cluster dividing center point
Figure GDA00024498053800000317
The density index of other base stations is
Figure GDA0002449805380000032
Then
Figure GDA0002449805380000033
1.3: judgment of
Figure GDA0002449805380000034
If yes, obtaining that the clustering number is L ═ j, and jumping to the step 1.4; if not, returning to the step 1.2, wherein,
Figure GDA0002449805380000035
determining the maximum value of the updated base station density index for the jth cluster central point,
Figure GDA00024498053800000318
the density index value of the 1 st cluster center point is delta, which is an influence factor;
1.4: the cluster number L and the cluster center point set obtained according to the step 1.3
Figure GDA0002449805380000036
Recording the coordinate value of the central point of each cluster as mujI.e. by
Figure GDA0002449805380000037
Clusters were formed by the following iterations:
① calculation
Figure GDA0002449805380000038
When c is going to(f)When j is the number, the cluster where the f-th base station is located is CjAnd calculating a criterion function
Figure GDA0002449805380000039
② updating the coordinate value of the center point of each cluster, i.e. the coordinate value of the center point of each cluster, according to the clustering result in step ①
Figure GDA00024498053800000310
Wherein, | CjAnd | represents the number of base stations in the jth cluster. Meanwhile, a new criterion function is calculated according to the new coordinate value of the cluster center point
Figure GDA00024498053800000311
If Enew＝EoldOutputting a clustering result to finish clustering operation; otherwise, it orders
Figure GDA00024498053800000312
Go back to step ① for re-clustering, where EoldRepresenting the old criterion function.
As a further technical scheme of the invention, the second step is specifically as follows:
2.1: initialization: setting the set of resource blocks used by each user to an empty set, i.e.
Figure GDA00024498053800000313
Central user and rate Rac0, edge user and rate Rae＝0；
2.2: the base station clusters divided in the step one are collected into a CU according to the associated usersjNumber of | CUjI descending order, and obtaining the cluster set of the base stations after reordering as { C1',C'2,...,C'LIn which CUjFor the jth base station cluster CjThe associated user set of (2);
2.3: the j base station cluster C 'in the reordered base station cluster set'jThe associated users sequentially perform first round resource block allocation, and the specific steps are as follows:
searching the user u, the base station m and the base station resource block l with the optimal channel state at the moment, namely meeting the condition
Figure GDA0002449805380000041
Figure GDA0002449805380000042
p∈CUjWherein, in the step (A),
Figure GDA0002449805380000043
indicating channel state information when the mth base station of the jth base station cluster allocates resource block l to user u,
Figure GDA0002449805380000044
indicating channel state information when the kth base station of the jth base station cluster allocates resource block N to user p, Nj,kDenotes a set of resource blocks, K, allocatable by the kth base station of the jth base station clusterjRepresenting the total number of base stations of the jth base station cluster;
① if the user U belongs to UcWherein, UcRepresenting the central user set, the resource block l of the mth base station of the jth base station cluster is allocated to the user u, namely omegau＝Ωu∪ { (j, m, l) }, and let N bej,m＝Nj,m-{l}，
Figure GDA0002449805380000045
Wherein the content of the first and second substances,
Figure GDA0002449805380000046
represents the connection relationship between the kth base station of the jth base station cluster and the user u, Nj,mRepresenting a set of resource blocks which can be allocated by the mth base station of the jth base station cluster; at the same time, order
Figure GDA0002449805380000047
Wherein the content of the first and second substances,
Figure GDA0002449805380000048
b is the bandwidth of the system resource block,
Figure GDA0002449805380000049
Figure GDA00024498053800000410
represents the transmission power of the kth base station of the jth base station cluster on resource block n,
Figure GDA00024498053800000411
indicating the channel gain of the user i and the kth base station of the jth base station cluster on resource block n,
Figure GDA00024498053800000412
represents the transmission power of the mth base station cluster on resource block n,
Figure GDA00024498053800000413
represents the channel gain, sigma, of user i and the mth base station of the ith base station cluster on the resource block n2Represents the power of additive white gaussian noise;
② if the user U belongs to UeWherein, UeRepresenting the edge user set, determining the cooperative base station p to meet the requirement
Figure GDA00024498053800000414
Allocating the resource block l of the mth base station of the jth base station cluster and the resource block l of the pth base station to the user u together, namely omegau＝Ωu∪ { (j, m, l), (j, p, l) }, and let N bej,m＝Nj,m-{l}，Nj,p＝Nj,p-{l}，
Figure GDA00024498053800000415
At the same time, order
Figure GDA00024498053800000416
Wherein the content of the first and second substances,
Figure GDA00024498053800000417
Figure GDA00024498053800000418
Figure GDA00024498053800000419
representing the connection relation between the mth base station of the jth base station cluster and a user i;
repeating the step 2.3 until all the associated users in the jth cluster perform resource allocation once, and entering the step 2.4;
2.4: if the resource blocks of all the base stations are completely distributed, obtaining a resource block distribution result; if it is
Figure GDA0002449805380000051
And then the resource block allocation result is obtained after the remaining resource blocks are continuously allocated according to the following two conditions:
① when
Figure GDA0002449805380000052
Then, find user u to satisfy
Figure GDA0002449805380000053
After determining user u, searching base station m and base station resource block l to satisfy
Figure GDA0002449805380000054
n∈Nj,kLet Ωu＝Ωu∪{(j,m,l)}，Nj,m＝Nj,m-{l}，
Figure GDA0002449805380000055
Order to
Figure GDA0002449805380000056
Wherein the content of the first and second substances,
Figure GDA00024498053800000513
the rate proportion fairness parameter is a central user and an edge user;
② if
Figure GDA0002449805380000057
Then user u is sought to be satisfied
Figure GDA0002449805380000058
After determining user u, searching base station m and base station resource block l to satisfy
Figure GDA0002449805380000059
n∈Nj,kAnd find the cooperative base station p to satisfy
Figure GDA00024498053800000510
Let omegau＝Ωu∪{(j,m,l),(j,p,l)}，Nj,m＝Nj,m-{l}，Nj,p＝Nj,p-{l}，
Figure GDA00024498053800000511
And order
Figure GDA00024498053800000512
As a further technical proposal of the invention.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects: the method separates the base station clustering and resource block distribution two sub-processes by using a step-by-step processing mode, thereby reducing the complexity of resource distribution. On one hand, aiming at the randomness of base station distribution, the method adopts a clustering method to obtain the optimal clustering result, and provides a more precise distribution space for the resource block distribution process; on the other hand, aiming at the wireless property of the resource block and the resource allocation condition in the cluster, the method can efficiently allocate the resource block in the resource pool, thereby obtaining the optimal resource allocation result. The method provided by the invention can effectively improve the user rate of the system and achieve the final goal of optimizing the whole network resources.
Drawings
FIG. 1 is a diagram of a cluster-based ultra-dense network system model according to the present invention.
Fig. 2 is a schematic diagram of resource block allocation in a specific cluster according to the present invention.
Fig. 3 is a flowchart of an embodiment of a method for allocating radio resources in a super-dense network based on dynamic clustering according to the present invention.
FIG. 4 is a graph comparing the performance of one embodiment of the method of the present invention with a prior art method.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the attached drawings:
fig. 1 is a system framework diagram of a super-dense network scenario for use in the proposed method of the present invention. Detailed description of the scenario of the present invention: the figure describes an ultra-dense LTE micro-cellular network, wherein F low-power-consumption small base stations are deployed in the network, the set of all the small base stations is marked as S, and a spatial distribution model of the network is that the network has density lambda in a two-dimensional planeSIndependent Homogeneous Poisson Point Process (HPPP). All small base stations share the radio resources of the same frequency band. All small base stations are set to be S, in order to reduce interference, the small base stations in the network are divided into L clusters, and C is set to be { C ═ C1,..,Cj,..,CLDenotes the set of all clusters, and
Figure GDA0002449805380000061
the total number of the small base stations in the jth cluster is Kj. In the LTE system, a base station adopts an OFDMA access method, a wireless resource is divided into C subcarriers in a frequency domain, and a bandwidth of each carrier is B, so that a physical wireless resource of the base station is abstracted into resource blocks in a virtual resource pool, the number of the resource blocks of each base station is N, and all the resources are uniformly controlled and configured by a Central Control Unit (CCU). The users are randomly distributed in the network with the distribution density of lambdaUAnd has aU＜λSAnd all user sets are denoted as U. Assuming that the user can accurately obtain the downlink channel state information, the user will preferentially select the base station with the maximum signal strength to become the main base station of the user according to the reference signal strengths of all the neighboring cells, and the cluster where the main base station is located is also the cluster where the user is accessed.
According to the above-mentioned sceneLet the kth base station in the jth cluster be sj,kSuppose a base station sj,kAnd allocating the resource block n to a user i, wherein the signal-to-interference-and-noise ratio of a receiving end of the user i is as follows:
Figure GDA0002449805380000062
wherein the content of the first and second substances,
Figure GDA0002449805380000063
representing base stations sj,kTransmit power on resource block n. Suppose the transmission power of a small base station is PmAnd the power is allocated equally for each resource block used, i.e.
Figure GDA0002449805380000064
Figure GDA0002449805380000065
Representing user i and base station sj,kChannel gain over resource block n; sigma2Representing the power of additive white gaussian noise.
Users are classified into two categories according to their location in the network, namely: a Center User (CU) and an Edge User (EU), which are respectively marked as UcAnd Ue. Assuming that a subscriber accesses a base station sj,kDefine γthTo distinguish the reference signal power thresholds for the center and edge users, the users can be classified by:
Figure GDA0002449805380000066
according to the user classification standard, the channel state of the central user is better, and signal transmission can be ensured only by using the resource of a single base station; the edge user is located at the edge of the base station and is interfered by a large amount of other base stations, the signal-to-interference-and-noise ratio of the receiving end is small, the signal quality is poor, and the transmission rate of the edge user needs to be improved by using a multi-base-station cooperative transmission mode. Therefore, for the edge user, a Coordinated Multiple Points-joint transmission (CoMP-JT) scheme is adopted, carriers of the same frequency of different base stations are used for transmitting signals for the user, and specific coding and modulation schemes can be adopted among the carriers for transmitting useful signals to eliminate interference. The transmission scheme can enhance the strength of useful signals, reduce the interference among users and improve the signal-to-interference-and-noise ratio of a user receiving end. In this scenario, the edge user only selects the base stations in the same cluster for cooperative transmission.
Fig. 2 illustrates an example of resource block allocation in a cluster. As can be seen from fig. 2, the base station cluster includes 3 small base stations, and physical resources of the base stations are abstracted to resource blocks in a resource pool and are allocated to 5 accessed users through the CCU in a unified manner. Wherein, the user 2 and the user 5 are central users and respectively obtain resources from the small base station 1 and the small base station 3; and user 1, user 3 and user 4 are edge users, and the main base station and the cooperative base station corresponding to the CCU user allocate corresponding resource blocks to meet the use condition of CoMP transmission. By using network virtualization technology, users in the network need not know the source of the physical resources used, but only care about their service experience.
The received signal when the edge user i uses the resource block n to transmit the signal is as follows:
Figure GDA0002449805380000071
wherein the content of the first and second substances,
Figure GDA0002449805380000072
indicating that user i received from base station s using resource block nj,kThe signal of (a); n is0Representing white gaussian noise;
Figure GDA0002449805380000073
for indicating variables, for indicating base stations sj,kThe value of the connection relation with the user i is shown as the formula (4):
Figure GDA0002449805380000074
when the edge user i uses the resource block n to transmit information, the signal-to-interference-and-noise ratio of the receiving end is as follows:
Figure GDA0002449805380000075
according to Shannon's formula, the achievable rate when an edge user i uses a resource block n is:
Figure GDA0002449805380000076
the edge user and rate are then:
Figure GDA0002449805380000077
for a central user i ∈ UcThe method comprises the following steps:
Figure GDA0002449805380000078
when the user uses the resource block n to transmit information, the signal-to-interference-and-noise ratio of the receiving end is as follows:
Figure GDA0002449805380000079
according to Shannon's formula, the achievable rate that can be obtained when the central user i uses resource block n is:
Figure GDA0002449805380000081
wherein, B is the system resource block bandwidth. The central user and rate are
Figure GDA0002449805380000082
In summary, the overall system sum rate is the edge user sum rate RaeAnd central user and rate RacAnd (3) the sum:
Figure GDA0002449805380000083
the invention aims at maximizing the system and the rate, and realizes the maximization of the overall network user and the rate through the optimized distribution of the base station resource blocks. Based on the thought, the following optimization problems are established:
optimizing the target:
Figure GDA0002449805380000084
constraint conditions are as follows: c1
Figure GDA0002449805380000085
C2
Figure GDA0002449805380000086
C3
Figure GDA0002449805380000087
C4
Figure GDA0002449805380000088
Wherein, C1 and C2 are clustering constraints, and C3 and C4 are orthogonality constraints of resource blocks. According to the optimization problem, the sum rate of the users, the clustering result C and the resource block allocation indicator variable
Figure GDA0002449805380000089
It is related. The above optimization problem belongs to 0-1 mixed integer non-linear programming, which is an NP-hard problem that is difficult to solve using traditional optimization methods. At the same time, due to the indication of the variables
Figure GDA00024498053800000810
The number is huge, the traditional method for hiding the enumeration is not suitable, and the new method is designed to solve the problems.
Fig. 3 is a flowchart of an embodiment of a radio resource allocation method for a dynamically clustered ultra-dense network according to the present invention. The method of this embodiment includes the following steps.
Step one, base station dynamic clustering.
In an ultra-dense wireless network scene, the distribution density of base stations is improved, the actual distribution positions of a large number of small base stations are not determined, and the base stations need to be clustered. The clustering of the base stations has the significance of providing a more refined resource allocation space for users, and each base station only needs to know the channel information of other base stations in the same cluster, so that the resource allocation in each cluster becomes more efficient, and the high complexity caused by resource allocation from the perspective of the whole network is reduced. If fixed regionalized clustering is adopted, the clustering result has the problem of different density degrees, and the expansion of the network and the flexible allocation of resources cannot be realized. And the dynamic clustering can change the number and scale of the clusters according to the actual distribution condition, and better plan the wireless resource allocation range in the network.
The invention dynamically clusters the base stations randomly distributed in the network, and clusters a large number of base stations in the network by using the improved K-means clustering method, thereby providing effective distribution space for resource block distribution in clusters of users with different modes. The realization method comprises the following steps: by using the improved K-means clustering method, the clustering process can be reasonably adjusted according to the actual distribution density of the base stations, the cluster center points and the cluster number are generated, and then the appropriate base stations are collected from the cluster center points to the periphery to obtain the final clustering result.
Definition of
Figure GDA00024498053800000917
Set of coordinates for all base stations, DfIs an index of density of base station f, rfIs the neighborhood radius of base station f. Delta is an influence factor, the value of which is related to the number of final clusters, in this scenario, delta is set to 0.5
The clustering method specifically comprises the following steps:
(1) calculating the density of all base stations to obtain density index
Figure GDA0002449805380000091
Wherein, the base station with the maximum density index is recorded as
Figure GDA0002449805380000092
Has a density index of
Figure GDA0002449805380000093
(2) Selecting the base station with the maximum density value, and recording as
Figure GDA0002449805380000094
Has a density index of
Figure GDA0002449805380000095
Then
Figure GDA0002449805380000096
Is defined as the jth cluster center point. At this time, the density indexes of other base stations except the cluster center point are updated
Figure GDA0002449805380000097
(3) Note the book
Figure GDA0002449805380000098
f≠m1,...,mjDetermine whether or not to satisfy
Figure GDA0002449805380000099
If yes, obtaining that the clustering number is L ═ j, and jumping to the step 4; if not, returning to the step 2.
(4) According to the cluster number L and the cluster center point set obtained in the step 3
Figure GDA00024498053800000910
Recording the coordinate value of the central point of each cluster as mujI.e. by
Figure GDA00024498053800000911
Clusters are formed by the following iterations:
① are calculated for all base stations F1, 2
Figure GDA00024498053800000912
When c is going to(f)When j is the cluster of base station f is Cj. Thereby forming L clusters C1,...,CL. Simultaneous calculation of criterion functions
Figure GDA00024498053800000913
② updating coordinate value of cluster center point, i.e. updating cluster center point according to the clustering result in ①
Figure GDA00024498053800000914
Meanwhile, a new criterion function is calculated according to the new coordinate value of the cluster center point
Figure GDA00024498053800000915
If Enew＝EoldThen the clustering operation is completed to obtain a clustering result C1,...,CL. Otherwise, the new cluster center coordinate value is calculated
Figure GDA00024498053800000916
The coordinate value as the new cluster center point is substituted into ① for re-clustering.
Clustering method based on the clustering result, scoring cluster CjIs a CUj，CUjThe associated base station set of user u in (1) is BSu. And after the dynamic clustering is finished, entering the next resource block allocation process.
And step two, resource block allocation.
When the resource block is allocated by taking the cluster as a unit, because the number of the associated users of each cluster is different, the resource block is preferentially allocated in the cluster with more associated users, which is beneficial to preferentially allocating the resource block with better channel state to more users, so that the cluster with more associated users can obtain more resources, and simultaneously, the interference in the network is also reduced. Meanwhile, in consideration of the requirement of CoMP transmission, the same resource block of different base stations needs to be synchronously allocated.
The invention discloses a resource block allocation method based on proportional fairness, which is characterized in that single base station resource allocation of a central user and in-cluster CoMP resource allocation of an edge user are synchronously processed according to a clustering result in the step one, and the resource block with the better channel state of the base station is preferentially allocated in a cluster where the user is located by the provided proportional fairness-based resource block allocation method, so that the received interference is reduced, and the proportional fairness among different modes is ensured. The realization method comprises the following steps: distributing all resource blocks to each cluster according to the clustering result obtained in the first step and the proportion of the number of the associated users of the cluster, then distributing the rest resource blocks to all users in an iterative manner, and simultaneously distributing the center users and the edge CoMP users to ensure the proportion fairness among the users. In addition, the method avoids the sharing of the resource blocks by adjacent clusters in the process of distributing the resource blocks to reduce the same frequency interference.
The method of the invention firstly conforms all resource blocks to CUjThe number of users (denoted as | CU)j|) the ratio is assigned in each cluster and then the remaining resource blocks are iteratively assigned to all users. Defining: omega denotes the set of resource blocks that have been allocated to a user,
Figure GDA0002449805380000106
is a central user and edge user rate proportional fairness parameter.
The resource block allocation method of the invention is specifically as follows:
1: initialization: setting the set of resource blocks used by each user to an empty set, i.e.
Figure GDA0002449805380000105
Central user and rate Rac0, edge user and rate Rae＝0。
2: base station cluster is grouped according to associated usersjNumber of | CUjI is arranged in descending order, and is reordered into { C1',C'2,...,C'L}。
3: sequentially allocating first-round resource blocks to associated users of a jth base station cluster, wherein the method comprises the following steps:
searching the user u, the base station m and the base station resource block l with the optimal channel state at the moment, namely meeting the condition
Figure GDA0002449805380000101
Figure GDA0002449805380000102
sj,k∈C'j,j＝1,...,L,k＝1,...,Kj,p∈CUjWherein, in the step (A),
Figure GDA0002449805380000103
and indicating the channel state information when the mth base station of the jth base station cluster allocates the resource block l to the u-th user. N is a radical ofj,kAnd represents a set of resource blocks which can be allocated by the kth base station of the jth base station cluster.
The users are divided into edge users and central users, and the following two situations are divided according to the types of the users:
① if the user U belongs to UcThen the resource is allocated to user u, i.e. Ωu＝Ωu∪{(j,m,l)}，Nj,m＝Nj,m-{l}，
Figure GDA0002449805380000104
And calculating R according to the formula (11)ac。
② if U ∈ UeDetermining the cooperative base station p to satisfy
Figure GDA0002449805380000111
Let omegau＝Ωu∪{(j,m,l),(j,p,l)}，Nj,m＝Nj,m-{l}，Nj,p＝Nj,p-{l}，
Figure GDA0002449805380000112
And calculating R according to the formula (7)ae. And returning to the step 3. Up to CUjAfter all the users have performed resource allocation once, go to step 4.
4: when in use
Figure GDA0002449805380000113
Then, the remaining resource blocks are allocated in two cases:
① when
Figure GDA0002449805380000114
Then, find user u to satisfy
Figure GDA0002449805380000115
After determining the user u, searching a base station m and a base station resource block l to satisfy
Figure GDA0002449805380000116
n∈Nj,kLet Ωu＝Ωu∪{(j,m,l)}，Nj,m＝Nj,m-{l}，
Figure GDA0002449805380000117
Calculating R according to the formula (11)ac。
② if
Figure GDA0002449805380000118
Then user u is sought to be satisfied
Figure GDA0002449805380000119
Fixing u, searching base station m and base station resource block l to satisfy
Figure GDA00024498053800001110
n∈Nj,kAnd coordinating base station p to satisfy
Figure GDA00024498053800001111
Let omegau＝Ωu∪{(j,m,l),(j,p,l)}，Nj,m＝Nj,m-{l}，Nj,p＝Nj,p-{l}，
Figure GDA00024498053800001112
And calculating R according to the formula (7)ae。
And when the resource allocation of all the base stations is complete, the circulation is exited to obtain the resource block allocation result.
By the method, the resource blocks of all the base stations in the resource pool are sequentially matched with all the users according to the set proportion of the central user and the edge user. At this point, the resource utilization in the network will reach a maximum, i.e. all resources have been allocated to the most suitable users. Meanwhile, the method avoids the sharing of the resource block by the adjacent clusters in the process of distributing the resource block by the CoMP transmission of the edge user, thereby reducing the interference and obtaining larger users and higher speed.
Fig. 4 is a comparison of the performance of the method of the present invention with conventional fixed clustering and random resource block allocation methods. It can be seen that, under the same system environment setting, with the increasing distribution density of small base stations, the resource allocation method provided by the invention can obtain higher users and higher rate.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can understand that the modifications or substitutions within the technical scope of the present invention are included in the scope of the present invention, and therefore, the scope of the present invention should be subject to the protection scope of the claims.

Claims (3)

1. The method for allocating the wireless resources of the ultra-dense network based on the dynamic clustering is characterized by comprising the following specific steps of:
step one, dynamic clustering of a base station: determining cluster center points and the number of clusters according to the distribution density of the base stations, and clustering the base stations which are randomly distributed in the network by adopting an improved K-means clustering method;
the method specifically comprises the following steps:
1.1: calculating the density index of each base station in the network:
Figure FDA0002449805370000011
wherein D isfIs a density index of the f-th base station, rfIs the neighborhood radius of the f-th base station, scfIs the coordinate of the f-th base station, scbIs the coordinate of the b-th base station, and F is the total number of base stations in the network;
1.2: the base station with the maximum density index value is recorded as
Figure FDA0002449805370000012
Its density is marked as
Figure FDA0002449805370000013
Then
Figure FDA0002449805370000014
Defining the jth cluster center point, and updating the cluster dividing center point
Figure FDA0002449805370000015
The density index of other base stations is
Figure FDA0002449805370000016
Then
Figure FDA0002449805370000017
1.3: judgment of
Figure FDA0002449805370000018
If yes, obtaining that the clustering number is L ═ j, and jumping to the step 1.4; if not, returning to the step 1.2, wherein,
Figure FDA0002449805370000019
determining the maximum value of the updated base station density index for the jth cluster central point,
Figure FDA00024498053700000110
the density index value of the 1 st cluster center point is delta, which is an influence factor;
1.4: the cluster number L and the cluster center point set obtained according to the step 1.3
Figure FDA00024498053700000111
Noting the coordinates of each cluster center pointValue of μjI.e. by
Figure FDA00024498053700000112
Clusters were formed by the following iterations:
① calculation
Figure FDA00024498053700000113
When c is going to(f)When j is the number, the cluster where the f-th base station is located is CjAnd calculating a criterion function
Figure FDA00024498053700000114
② updating the coordinate value of the center point of each cluster, i.e. the coordinate value of the center point of each cluster, according to the clustering result in step ①
Figure FDA00024498053700000115
Wherein, | CjL represents the number of base stations in the jth cluster; meanwhile, a new criterion function is calculated according to the new coordinate value of the cluster center point
Figure FDA00024498053700000116
If Enew＝EoldOutputting a clustering result to finish clustering operation; otherwise, it orders
Figure FDA00024498053700000117
Go back to step ① for re-clustering, where EoldRepresents the old criteria function;
step two, resource block allocation: and B, according to the clustering result in the step one, adopting a proportional fair-based resource block allocation method to allocate resource blocks, allocating the resource blocks of all base stations in the virtual resource pool to all users, and processing edge users according to the allocation requirement of multipoint cooperative transmission, thereby completing resource allocation.
2. The method for allocating the radio resources of the ultra-dense network based on the dynamic clustering as claimed in claim 1, wherein the ultra-dense network is a densely distributed LTE small base station, wherein the spatial distribution model of the base station is an independent uniform poisson point process in a two-dimensional plane, and all the base stations adopt an OFDMA access method.
3. The method for allocating radio resources in a very dense network based on dynamic clustering according to claim 1, wherein step two is specifically:
2.1: initialization: setting the set of resource blocks used by each user to an empty set, i.e.
Figure FDA0002449805370000021
Central user and rate Rac0, edge user and rate Rae＝0；
2.2: the base station clusters divided in the step one are collected into a CU according to the associated usersjNumber of | CUjL descending order, and obtaining a cluster of base stations which are reordered as { C'1,C'2,...,C'LIn which CUjFor the jth base station cluster CjThe associated user set of (2);
2.3: the j base station cluster C 'in the reordered base station cluster set'jThe associated users sequentially perform first round resource block allocation, and the specific steps are as follows:
searching the user u, the base station m and the base station resource block l with the optimal channel state at the moment, namely meeting the condition
Figure FDA0002449805370000022
Figure FDA0002449805370000023
Wherein the content of the first and second substances,
Figure FDA0002449805370000024
indicating channel state information when the mth base station of the jth base station cluster allocates resource block l to user u,
Figure FDA0002449805370000025
denotes the jthChannel state information when the kth base station of a cluster of base stations allocates a resource block N to a user p, Nj,kDenotes a set of resource blocks, K, allocatable by the kth base station of the jth base station clusterjRepresenting the total number of base stations of the jth base station cluster;
① if the user U belongs to UcWherein, UcRepresenting the central user set, the resource block l of the mth base station of the jth base station cluster is allocated to the user u, namely omegau＝Ωu∪ { (j, m, l) }, and let N bej,m＝Nj,m-{l}，
Figure FDA0002449805370000026
Wherein the content of the first and second substances,
Figure FDA0002449805370000027
represents the connection relationship between the kth base station of the jth base station cluster and the user u, Nj,mRepresenting a set of resource blocks which can be allocated by the mth base station of the jth base station cluster; at the same time, order
Figure FDA0002449805370000028
Wherein the content of the first and second substances,
Figure FDA0002449805370000029
n denotes the number of resource blocks per base station, B is the bandwidth of the system resource blocks,
Figure FDA00024498053700000210
Figure FDA00024498053700000211
represents the transmission power of the kth base station of the jth base station cluster on resource block n,
Figure FDA00024498053700000212
indicating the channel gain of the user i and the kth base station of the jth base station cluster on resource block n,
Figure FDA0002449805370000031
represents the transmission power of the mth base station cluster on resource block n,
Figure FDA0002449805370000032
represents the channel gain, sigma, of user i and the mth base station of the ith base station cluster on the resource block n2Represents the power of additive white gaussian noise;
② if the user U belongs to UeWherein, UeRepresenting the edge user set, determining the cooperative base station p to meet the requirement
Figure FDA0002449805370000033
Allocating the resource block l of the mth base station of the jth base station cluster and the resource block l of the pth base station to the user u together, namely omegau＝Ωu∪ { (j, m, l), (j, p, l) }, and let N bej,m＝Nj,m-{l}，Nj,p＝Nj,p-{l}，
Figure FDA0002449805370000034
At the same time, order
Figure FDA0002449805370000035
Wherein the content of the first and second substances,
Figure FDA0002449805370000036
Figure FDA0002449805370000037
Figure FDA0002449805370000038
representing the connection relation between the mth base station of the jth base station cluster and a user i;
repeating the step 2.3 until all the associated users in the jth cluster perform resource allocation once, and entering the step 2.4;
2.4: if the resource blocks of all the base stations are completely distributed, obtaining a resource block distribution result; if it is
Figure FDA0002449805370000039
And then the resource block allocation result is obtained after the remaining resource blocks are continuously allocated according to the following two conditions:
① when
Figure FDA00024498053700000310
Then, find user u to satisfy
Figure FDA00024498053700000311
After determining user u, searching base station m and base station resource block l to satisfy
Figure FDA00024498053700000312
Let omegau＝Ωu∪{(j,m,l)}，Nj,m＝Nj,m-{l}，
Figure FDA00024498053700000313
Order to
Figure FDA00024498053700000314
Wherein the content of the first and second substances,
Figure FDA00024498053700000315
the rate proportion fairness parameter is a central user and an edge user;
Figure FDA00024498053700000316
Figure FDA00024498053700000317
indicating the channel gain of the user u and the kth base station of the jth base station cluster on the resource block n,
Figure FDA00024498053700000318
Figure FDA00024498053700000319
representing users r and jChannel gain of the kth base station of each base station cluster on a resource block n;
② if
Figure FDA00024498053700000320
Then user u is sought to be satisfied
Figure FDA00024498053700000321
After determining user u, searching base station m and base station resource block l to satisfy
Figure FDA00024498053700000322
And find the cooperative base station p to satisfy
Figure FDA00024498053700000323
Let omegau＝Ωu∪{(j,m,l),(j,p,l)}，Nj,m＝Nj,m-{l}，Nj,p＝Nj,p-{l}，
Figure FDA00024498053700000324
And order
Figure FDA00024498053700000325
sj,kThe k base station, BS, representing the j base station clusteruRepresenting the set of associated base stations for user u.
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